Homecare-CONCERN: Building risk models for preventable hospitalizations and emergency department visits in homecare

家庭护理-关注：建立家庭护理中可预防的住院和急诊就诊的风险模型

• 批准号: 10264874
• 负责人: Maxim Topaz
• 金额: $ 36.94万
• 依托单位: VISITING NURSE SERVICE OF NEW YORK
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-30 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10264874
• 关键词: Homecare; CONCERN; Building; risk; models

Project Summary/Abstract Every year, more than 11,000 homecare agencies across the United States provide care to more than 5 million older adults. Currently, about one in three homecare patients are hospitalized or visit an emergency department (ED) during the 30-60 day homecare episode. Up to 40% of these events are preventable with appropriate and timely care. In our pilot work, we developed a risk prediction model (called Homecare- CONCERN) that accurately identified patients at risk for hospital admission and ED visits solely from homecare clinical notes using NLP. This study brings together an interdisciplinary team of experts in homecare, data science, nursing and risk model development to explore whether cutting-edge data science approaches can improve timely identification of patients at risk in homecare. Our specific aims are to: 1. Further develop and validate a preventable hospitalization or ED visit risk prediction model (Homecare- CONCERN). We will apply traditional (time varying Cox regression) and cutting-edge time-sensitive analytical methods (Deep Survival Analysis and Long-Short Term Memory Neural Network) for risk model development. 2. Prepare Homecare-CONCERN for clinical trial via pilot testing. We will apply user centered design to develop Homecare-CONCERN clinical decision support tool and pilot test the tool for clinical validity and acceptability. 3. Inform the future implementation of Homecare-CONCERN clinical decision support tool in the homecare setting. We will examine if all risk elements can be mapped to a data standard (Fast Healthcare Interoperability Resources - FHIR) and conduct interviews with key informants across the US about current readiness, barriers and facilitators, and implementation strategies for adopting such tools in homecare setting. This proposal addresses the AHRQ program announcement (PA-18-795) to harness data to improve healthcare quality and patient outcomes. The study will build a first-of-a-kind clinical decision support system to trigger timely and personalized alerts about concerning patient trends that activate appropriate and timely care to prevent avoidable hospitalizations and ED visits from homecare.

项目总结/摘要 每年，全美11,000多家家庭护理机构为500多万名 老年人目前，大约三分之一的家庭护理患者住院或急诊 在30-60天的家庭护理期内，在急诊科（艾德）接受治疗。这些事件中有40%是可以预防的， 适当和及时的护理。在我们的试点工作中，我们开发了一个风险预测模型（称为家庭护理- CONCERN），仅通过家庭护理准确识别存在入院和艾德访视风险的患者 使用NLP的临床笔记。这项研究汇集了一个跨学科的家庭护理专家团队，数据 科学，护理和风险模型开发，以探索尖端的数据科学方法是否可以 更及时地识别家庭护理中的高危病人。我们的具体目标是： 1.进一步开发和验证可预防的住院或艾德就诊风险预测模型（家庭护理- 关注）。我们将应用传统的（时变考克斯回归）和尖端的时间敏感 风险模型的分析方法（深度生存分析和长短期记忆神经网络） 发展 2.通过试点测试为临床试验准备家庭护理关注。我们将应用以用户为中心的设计， 开发Homecare-Concern临床决策支持工具，并对该工具进行临床有效性的试点测试， 可接受性 3.告知未来在家庭护理中实施家庭护理-关注临床决策支持工具 设置.我们将检查是否所有风险元素都可以映射到数据标准（快速医疗保健 互操作性资源- FHIR），并就当前的 在家庭护理中采用这些工具的准备情况、障碍和促进因素以及实施战略 设置. 该提案涉及AHRQ计划公告（PA-18-795），以利用数据改善 医疗质量和患者结果。这项研究将建立一个一流的临床决策支持 系统触发关于患者趋势的及时和个性化警报， 及时护理，以防止可避免的住院和家庭护理的艾德就诊。